

import argparse
import struct
import unittest





class       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							bytes				) ->None:
									snake_case_  =      data

									# Initialize hash values
									snake_case_  =      [
									    0x6_a_0_9_e_6_6_7,
									    0xb_b_6_7_a_e_8_5,
									    0x3_c_6_e_f_3_7_2,
									    0xa_5_4_f_f_5_3_a,
									    0x5_1_0_e_5_2_7_f,
									    0x9_b_0_5_6_8_8_c,
									    0x1_f_8_3_d_9_a_b,
									    0x5_b_e_0_c_d_1_9,
									]

									# Initialize round constants
									snake_case_  =      [
									    0x4_2_8_a_2_f_9_8,
									    0x7_1_3_7_4_4_9_1,
									    0xb_5_c_0_f_b_c_f,
									    0xe_9_b_5_d_b_a_5,
									    0x3_9_5_6_c_2_5_b,
									    0x5_9_f_1_1_1_f_1,
									    0x9_2_3_f_8_2_a_4,
									    0xa_b_1_c_5_e_d_5,
									    0xd_8_0_7_a_a_9_8,
									    0x1_2_8_3_5_b_0_1,
									    0x2_4_3_1_8_5_b_e,
									    0x5_5_0_c_7_d_c_3,
									    0x7_2_b_e_5_d_7_4,
									    0x8_0_d_e_b_1_f_e,
									    0x9_b_d_c_0_6_a_7,
									    0xc_1_9_b_f_1_7_4,
									    0xe_4_9_b_6_9_c_1,
									    0xe_f_b_e_4_7_8_6,
									    0x0_f_c_1_9_d_c_6,
									    0x2_4_0_c_a_1_c_c,
									    0x2_d_e_9_2_c_6_f,
									    0x4_a_7_4_8_4_a_a,
									    0x5_c_b_0_a_9_d_c,
									    0x7_6_f_9_8_8_d_a,
									    0x9_8_3_e_5_1_5_2,
									    0xa_8_3_1_c_6_6_d,
									    0xb_0_0_3_2_7_c_8,
									    0xb_f_5_9_7_f_c_7,
									    0xc_6_e_0_0_b_f_3,
									    0xd_5_a_7_9_1_4_7,
									    0x0_6_c_a_6_3_5_1,
									    0x1_4_2_9_2_9_6_7,
									    0x2_7_b_7_0_a_8_5,
									    0x2_e_1_b_2_1_3_8,
									    0x4_d_2_c_6_d_f_c,
									    0x5_3_3_8_0_d_1_3,
									    0x6_5_0_a_7_3_5_4,
									    0x7_6_6_a_0_a_b_b,
									    0x8_1_c_2_c_9_2_e,
									    0x9_2_7_2_2_c_8_5,
									    0xa_2_b_f_e_8_a_1,
									    0xa_8_1_a_6_6_4_b,
									    0xc_2_4_b_8_b_7_0,
									    0xc_7_6_c_5_1_a_3,
									    0xd_1_9_2_e_8_1_9,
									    0xd_6_9_9_0_6_2_4,
									    0xf_4_0_e_3_5_8_5,
									    0x1_0_6_a_a_0_7_0,
									    0x1_9_a_4_c_1_1_6,
									    0x1_e_3_7_6_c_0_8,
									    0x2_7_4_8_7_7_4_c,
									    0x3_4_b_0_b_c_b_5,
									    0x3_9_1_c_0_c_b_3,
									    0x4_e_d_8_a_a_4_a,
									    0x5_b_9_c_c_a_4_f,
									    0x6_8_2_e_6_f_f_3,
									    0x7_4_8_f_8_2_e_e,
									    0x7_8_a_5_6_3_6_f,
									    0x8_4_c_8_7_8_1_4,
									    0x8_c_c_7_0_2_0_8,
									    0x9_0_b_e_f_f_f_a,
									    0xa_4_5_0_6_c_e_b,
									    0xb_e_f_9_a_3_f_7,
									    0xc_6_7_1_7_8_f_2,
									]

									snake_case_  =      self.preprocessing(self.data				)
									self.final_hash()







			@staticmethod
			def 							snake_case__(	_UpperCamelCase					:							bytes				) ->bytes:
									snake_case_  =      B'''\x80''' + (B'''\x00''' * (6_3 - (len(_UpperCamelCase				) + 8) % 6_4))
									snake_case_  =      struct.pack('''>Q'''				,  (len(_UpperCamelCase				) * 8)				)
									return data + padding + big_endian_integer







			def 							snake_case__(	self					:							Optional[Any]				) ->None:
									# Convert into blocks of 64 bytes
									snake_case_  =      [
									    self.preprocessed_data[x : x + 6_4]
									    for x in range(0				,  len(self.preprocessed_data				)				,  6_4				)
									]

									for block in self.blocks:
															# Convert the given block into a list of 4 byte integers
															snake_case_  =      list(struct.unpack('''>16L'''				,  _UpperCamelCase				)				)
															# add 48 0-ed integers
															words += [0] * 4_8

															snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_  =      self.hashes

															for index in range(0				,  6_4				):
																					if index > 1_5:
																											# modify the zero-ed indexes at the end of the array
																											snake_case_  =      (
																											    self.ror(words[index - 1_5]				,  7				)
																											    ^ self.ror(words[index - 1_5]				,  1_8				)
																											    ^ (words[index - 1_5] >> 3)
																											)
																											snake_case_  =      (
																											    self.ror(words[index - 2]				,  1_7				)
																											    ^ self.ror(words[index - 2]				,  1_9				)
																											    ^ (words[index - 2] >> 1_0)
																											)

																											snake_case_  =      (
																											    words[index - 1_6] + sa + words[index - 7] + sa
																											) % 0x1_0_0_0_0_0_0_0_0

																					# Compression
																					snake_case_  =      self.ror(_UpperCamelCase				,  6				) ^ self.ror(_UpperCamelCase				,  1_1				) ^ self.ror(_UpperCamelCase				,  2_5				)
																					snake_case_  =      (e & f) ^ ((~e & 0xf_f_f_f_f_f_f_f) & g)
																					snake_case_  =      (
																					    h + sa + ch + self.round_constants[index] + words[index]
																					) % 0x1_0_0_0_0_0_0_0_0
																					snake_case_  =      self.ror(_UpperCamelCase				,  2				) ^ self.ror(_UpperCamelCase				,  1_3				) ^ self.ror(_UpperCamelCase				,  2_2				)
																					snake_case_  =      (a & b) ^ (a & c) ^ (b & c)
																					snake_case_  =      (sa + maj) % 0x1_0_0_0_0_0_0_0_0

																					snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_  =      (
																					    g,
																					    f,
																					    e,
																					    ((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
																					    c,
																					    b,
																					    a,
																					    ((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
																					)

															snake_case_  =      [a, b, c, d, e, f, g, h]

															# Modify final values
															snake_case_  =      [
															    ((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
															    for index, element in enumerate(self.hashes				)
															]

									snake_case_  =      ''''''.join([hex(_UpperCamelCase				)[2:].zfill(8				) for value in self.hashes]				)







			def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				) ->int:
									return 0xf_f_f_f_f_f_f_f & (value << (3_2 - rotations)) | (value >> rotations)






class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			def 							snake_case__(	self					:							Any				) ->None:
									import hashlib

									snake_case_  =      bytes('''Test String'''				,  '''utf-8'''				)
									self.assertEqual(SHAaaa(_UpperCamelCase				).hash				,  hashlib.shaaaa(_UpperCamelCase				).hexdigest()				)






def     __SCREAMING_SNAKE_CASE   ():
						import doctest

						doctest.testmod()

						snake_case_  =      argparse.ArgumentParser()
						parser.add_argument(
						    '''-s'''    ,						'''--string'''    ,						dest='''input_string'''    ,						default='''Hello World!! Welcome to Cryptography'''    ,						help='''Hash the string'''    ,						)
						parser.add_argument(
						    '''-f'''    ,						'''--file'''    ,						dest='''input_file'''    ,						help='''Hash contents of a file'''			)

						snake_case_  =      parser.parse_args()

						snake_case_  =      args.input_string

						# hash input should be a bytestring
						if args.input_file:
												with open(args.input_file    ,						'''rb'''			) as f:
																		snake_case_  =      f.read()
						else:
												snake_case_  =      bytes(SCREAMING_SNAKE_CASE__    ,						'''utf-8'''			)

						print(SHAaaa(SCREAMING_SNAKE_CASE__			).hash			)


if __name__ == "__main__":
					main()




'''simple docstring'''

import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


SCREAMING_SNAKE_CASE__     :						List[Any]	=  logging.get_logger(__name__)

enable_full_determinism()

class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Dict									=      UNetaDModel
			a_	: List[Any]									=      '''sample'''






			@property
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10]						).to(_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     ={
					    'block_out_channels': (32, 64),
					    'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
					    'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
					    'attention_head_dim': 3,
					    'out_channels': 3,
					    'in_channels': 3,
					    'layers_per_block': 2,
					    'sample_size': 32,
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict





class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Union[str, Any]									=      UNetaDModel
			a_	: Optional[Any]									=      '''sample'''






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =4
					snake_case__	     =4
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10]						).to(_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (4, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Dict:
					return (4, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'sample_size': 32,
					    'in_channels': 4,
					    'out_channels': 4,
					    'layers_per_block': 2,
					    'block_out_channels': (32, 64),
					    'attention_head_dim': 32,
					    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
					    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)

					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						).sample

					assert image is not None, "Make sure output is not None"






			@unittest.skipIf(torch_device != 'cuda'					,   'This test is supposed to run on GPU'						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						).sample

					assert image is not None, "Make sure output is not None"






			@unittest.skipIf(torch_device != 'cuda'					,   'This test is supposed to run on GPU'						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)
					model_accelerate.to(_UpperCAmelCase						)
					model_accelerate.eval()

					snake_case__	     =torch.randn(
					    1					,   model_accelerate.config.in_channels					,   model_accelerate.config.sample_size					,   model_accelerate.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =noise.to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10] * noise.shape[0]						).to(_UpperCAmelCase						)

					snake_case__	     =model_accelerate(_UpperCAmelCase					,   _UpperCAmelCase						)['sample']

					# two models don't need to stay in the device at the same time
					del model_accelerate
					torch.cuda.empty_cache()
					gc.collect()

					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained(
					    'fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase					,   low_cpu_mem_usage=_UpperCAmelCase						)
					model_normal_load.to(_UpperCAmelCase						)
					model_normal_load.eval()
					snake_case__	     =model_normal_load(_UpperCAmelCase					,   _UpperCAmelCase						)['sample']

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-3						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'						)
					model.eval()
					model.to(_UpperCAmelCase						)

					snake_case__	     =torch.randn(
					    1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =noise.to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10] * noise.shape[0]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -1, -3:, -3:].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-3						)						)





class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: List[str]									=      UNetaDModel
			a_	: Optional[int]									=      '''sample'''






			@property
			def 						_lowercase  (     self					,   _UpperCAmelCase=(32, 32)						)							->							Tuple:
					snake_case__	     =4
					snake_case__	     =3

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [10]						).to(dtype=torch.intaa					,   device=_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Optional[Any]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'block_out_channels': [32, 64, 64, 64],
					    'in_channels': 3,
					    'layers_per_block': 1,
					    'out_channels': 3,
					    'time_embedding_type': 'fourier',
					    'norm_eps': 1E-6,
					    'mid_block_scale_factor': math.sqrt(2.0						),
					    'norm_num_groups': None,
					    'down_block_types': [
					        'SkipDownBlock2D',
					        'AttnSkipDownBlock2D',
					        'SkipDownBlock2D',
					        'SkipDownBlock2D',
					    ],
					    'up_block_types': [
					        'SkipUpBlock2D',
					        'SkipUpBlock2D',
					        'AttnSkipUpBlock2D',
					        'SkipUpBlock2D',
					    ],
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'					,   output_loading_info=_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =self.dummy_input
					snake_case__	     =floats_tensor((4, 3) + (256, 256)						).to(_UpperCAmelCase						)
					snake_case__	     =noise
					snake_case__	     =model(**_UpperCAmelCase						)

					assert image is not None, "Make sure output is not None"






			@slow
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'						)
					model.to(_UpperCAmelCase						)

					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(256, 256)

					snake_case__	     =torch.ones((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [1E-4]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'						)
					model.to(_UpperCAmelCase						)

					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =torch.ones((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [1E-4]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					# not required for this model
					pass




